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Entropy - learning decision trees:
Through putting together a decision of tree is all a matter of choosing that attribute to test at each node in the tree. Further we shall define a measure that is information gain that will be used to decide which attribute to test at each node. By considering through information gain is itself calculated utilising a measure called entropy that we first define for the case of a binary decision problem so then define for the general case.
As per given a binary categorisation like C, and a set of examples, S, that are utilising the proportion of examples categorised as positive by C is p+ or the proportion of examples categorised like negative by C is p-, then the entropy of S is as:
Now next here instantly we defined entropy first for a binary decision problem is easier to get an impression of what it is trying to calculate. As Tom Mitchell puts this quite well: like
"In order to define an information gain precisely so we begin by defining a measure commonly utilising in information theory that is called entropy in which characterizes the (im)purity of as an arbitrary collection of examples."
Which datatype cannot be used to define parameters. Type F datatype is not used to explain parameters.
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